Papers with Code 아카이브

Multi-task Language Understanding

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하위 태스크: 0
평가표: 5
보존된 평가 기록: 79

The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. https://arxiv.org/pdf/2009.03300.pdf

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/gpt_neox_japanese

GPT-NeoX-20B: An Open-Source Autoregressive Language Model

https://github.com/2796gaurav/human-eval

Evaluating Large Language Models Trained on Code

https://github.com/8023looker/med-rr

Measuring Massive Multitask Language Understanding

https://github.com/AUCOHL/RTL-Repo

GPT-4 Technical Report

https://github.com/AdamStein97/Semi-Supervised-BERT-NER

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/AhmedYounes94/Semi-supervised-BERT-NER

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/CLUEbenchmark/CLUE

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/CalumPerrio/WNUT-2020

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/DataScienceNigeria/ALBERT-for-Natural-Language-Processing

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/EightRice/atn_GPT-3

Language Models are Few-Shot Learners

https://github.com/EleutherAI/gpt-neo

Language Models are Few-Shot Learners

https://github.com/EleutherAI/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/EleutherAI/lm_evaluation_harness

Language Models are Few-Shot Learners

https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction-roBERTa-5fold

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/GeorgeLuImmortal/Hierarchical-BERT-Model-with-Limited-Labelled-Data

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/IBM/Dromedary

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/IndicoDataSolutions/finetune

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/Karthik-Bhaskar/Context-Based-Question-Answering

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/KnightZhang625/BERT_TF

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/Leeroo-AI/mergoo

Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM

https://github.com/Lightning-AI/lit-gpt

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/Lightning-AI/lit-llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/MS-P3/code5/tree/main/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/MS-P3/code5/tree/main/llama2

LLaMA: Open and Efficient Foundation Language Models

https://github.com/MS-P3/code7/tree/main/xlm_roberta_xl

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/Mind23-2/MindCode-131

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/Mind23-2/MindCode-138

Language Models are Few-Shot Learners

https://github.com/Mind23-2/MindCode-140

LLaMA: Open and Efficient Foundation Language Models

https://github.com/MindSpore-paper-code-3/code9/tree/main/albert

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/MindSpore-scientific-2/code-14/tree/main/gpt_neox_japanese

GPT-NeoX-20B: An Open-Source Autoregressive Language Model

https://github.com/MindSpore-scientific-2/code-14/tree/main/llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/NVIDIA/NeMo-Curator

Language Models are Few-Shot Learners

https://github.com/NathanDuran/Sentence-Encoding-for-DA-Classification

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/albert/modeling.py

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/roberta/modeling.py

RoBERTa: A Robustly Optimized BERT Pretraining Approach

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.